AI-103 Implement Generative AI And Agentic Solutions Practice Test 6 — 15 Questions

Question 1

You are designing a customer service chatbot using Azure OpenAI. You need to ensure that the model refuses to answer questions outside the scope of your company's products. Where should you define this boundary instruction?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

In the system message. The system message is correct because it is the designated channel for persistent, high-priority instructions that define the model's persona, scope, and refusal behavior across all turns of a conversation. Placing the boundary instruction there ensures it applies before and throughout the interaction, and it is the standard mechanism for scoping a chatbot to a domain. This is the architecturally correct location for guardrails that must not be overridden by user input.

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In the temperature setting

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In the user prompt prefix

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In the max_tokens parameter configuration

Question 2

You are integrating Azure OpenAI into an enterprise application. You need to prevent prompt injection attacks where users attempt to override the system instructions. Which approach provides the most robust defense?

Accepted Answer

Implement an input guardrail using Azure AI Content Safety to detect and block jailbreak attempts before they reach the model.. Azure AI Content Safety's Prompt Shields (jailbreak detection) is purpose-built to detect and block prompt injection and jailbreak attempts at the input boundary before they reach the model. This provides a robust, model-agnostic defense layer that is maintained and updated by Microsoft against emerging attack patterns. Input filtering is the recommended first line of defense in Microsoft's defense-in-depth guidance for LLM applications.

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Set the model frequency penalty to 2.0 to penalize repeated malicious phrases.

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Append 'Ignore previous instructions' at the end of every user message.

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Encode all user input in Base64 before sending it to the model.

Question 3

You are orchestrating a multi-agent system in Microsoft Foundry where Agent A generates code and Agent B reviews it. Agent B frequently gets stuck in a loop trying to optimize minor syntax formatting. How should you design the agentic workflow to prevent this infinite refinement loop?

Accepted Answer

Configure a maximum turn counter and state transition condition in the orchestrator workflow to terminate the loop after a set threshold.. Configuring a maximum turn counter and a state transition condition in the orchestrator terminates the loop after a defined threshold, preventing infinite refinement. This is the standard pattern for bounded agentic workflows, ensuring the system exits gracefully when convergence is not reached. It directly addresses the runaway loop without degrading agent capability.

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Disable function calling for Agent B so it can only output text responses.

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Increase the model's presence penalty so Agent B is forced to generate novel review comments.

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Use a smaller model for Agent B to reduce the quality of its reviews.

Question 4

You are evaluating a generative AI application in Azure AI Foundry using automated metrics. You need to measure how well the generated answers are supported by the retrieved source documents. Which evaluation metric should you use?

Accepted Answer

Groundedness. Groundedness measures how well a model's generated response is supported by the retrieved source documents, which is exactly the requirement in a RAG-style generative AI application. Azure AI Foundry's evaluation SDK provides a groundedness metric that uses an LLM judge to check whether claims in the answer are entailed by the provided context.

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Word Error Rate (WER)

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BLEU score

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Perplexity

Question 5

You are designing an agentic workflow where an agent needs to maintain conversational context and remember user preferences across multiple disparate sessions over several weeks. Which architectural component should you implement?

Accepted Answer

A persistent semantic memory store integrated with the agent's context pipeline.. Maintaining conversational context and user preferences across disparate sessions over weeks requires a persistent memory layer that survives beyond a single session or context window. A persistent semantic memory store integrated with the agent's context pipeline allows the agent to retrieve relevant past interactions and preferences via embeddings, enabling long-term personalization. This is the standard architectural pattern for long-horizon agent memory.

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Increasing the model context window to 1,000,000 tokens for every request.

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Using a stateless function with zero memory persistence.

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Storing the entire conversation history in a local client-side cookie.

Question 6

You are configuring an Azure AI Search index for use with Azure OpenAI On Your Data. The index contains technical documentation with complex nested tables. Which search configuration will yield the best retrieval performance for semantic queries?

Accepted Answer

Enable semantic search alongside vector and keyword search (hybrid search).. Hybrid search combines keyword (BM25/Lucene) scoring with vector similarity, and semantic search adds a Microsoft-hosted reranker (based on deep learning models) on top of the merged results. For complex nested tables in technical documentation, keyword matching alone misses paraphrased queries, while pure vector search can lose exact identifiers; the semantic reranker then reorders the top ~50 results for relevance. This layered approach yields the best retrieval quality for RAG grounding in Azure OpenAI On Your Data.

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Configure a pure OData filter index without vector embeddings.

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Use simple query syntax with exact phrase matching only.

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Disable all vector fields and rely exclusively on traditional Lucene scoring.

Question 7

You are preparing a prompt template for a summarization task. You want to provide three distinct examples of input text and expected summaries before asking the model to process the target text. What is this prompting technique called?

Accepted Answer

Few-shot prompting. Few-shot prompting provides a small number of input-output examples (typically 2-5) in the prompt to demonstrate the desired task before asking the model to perform it on new input. Three examples of input text and expected summaries is the textbook definition of few-shot summarization prompting.

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Retrieval-augmented generation

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Chain-of-thought prompting

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Zero-shot prompting

Question 8

You are implementing a RAG pipeline where documents are chunked before embedding generation. You notice that semantic sentences are frequently split across chunk boundaries, resulting in degraded retrieval quality. Which chunking strategy should you implement to resolve this?

Accepted Answer

Sliding window chunking with token overlap. Sliding window chunking with token overlap is correct because it preserves semantic continuity by carrying a portion of the previous chunk's tokens into the next chunk. This overlap ensures that sentences or ideas spanning a boundary appear intact in at least one chunk, so the embedding captures the full semantic unit. It directly addresses the root cause of degraded retrieval: fragmented context at chunk edges.

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Fixed-character chunking with zero overlap

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Paragraph-only splitting with maximum size limits of 10,000 tokens

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Random token slicing

Question 9

You are deploying an Azure OpenAI model in Azure AI Foundry and need to restrict access so that only specific virtual networks within your enterprise can invoke the model endpoint. Which feature should you configure?

Accepted Answer

Azure Private Link with private endpoints. Azure Private Link with private endpoints creates a private IP address for the Azure OpenAI endpoint inside your virtual network, so traffic never traverses the public internet. By disabling public network access and allowing only specific VNets via private endpoints, you restrict model invocation to those enterprise networks. This is the correct feature for network-level access restriction to an Azure AI Foundry model endpoint.

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API key rotation policies

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Cross-Origin Resource Sharing (CORS) headers

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Model rate limit quotas

Question 10

You are configuring an agent in Azure AI Foundry that needs to perform mathematical calculations accurately. Large language models often make arithmetic errors. How should you equip the agent to handle calculations reliably?

Accepted Answer

Provide a code interpreter tool so the model can write and execute Python code for calculations.. Large language models are probabilistic token predictors and frequently make arithmetic errors, especially with multi-digit operations. Providing a code interpreter tool lets the agent generate and execute Python code, delegating computation to a deterministic runtime that returns exact results. This is the reliable pattern for math in agentic workflows.

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Set the model temperature to 0.0 and ask it to guess the result.

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Increase the presence penalty to force new tokens.

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Instruct the model in the system prompt to double-check its arithmetic mentally.

Question 11

You are designing a generative AI application that processes user uploaded documents. You must ensure that Personally Identifiable Information (PII) such as Social Security numbers are automatically redacted before the text is sent to the Azure OpenAI model. Which service should you integrate into your pipeline?

Accepted Answer

Azure AI Language service (PII detection and redaction). Azure AI Language provides a prebuilt PII detection and redaction capability that identifies entities like Social Security numbers, credit card numbers, and names, and can mask or redact them in text. Integrating it into the pipeline before sending text to Azure OpenAI ensures sensitive data never reaches the model. This is the purpose-built service for the requirement.

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Azure AI Translator

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Azure AI Vision OCR

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Azure Monitor Application Insights

Question 12

You are monitoring your Azure OpenAI deployment in Azure AI Foundry. You notice an increase in HTTP 429 status codes. What does this error indicate?

Accepted Answer

Rate limit or token quota exceeded (Too Many Requests). HTTP 429 'Too Many Requests' indicates the client has exceeded the rate limit or token quota assigned to the Azure OpenAI deployment. This occurs when requests per minute (RPM) or tokens per minute (TPM) surpass the deployment's quota, triggering throttling until the window resets.

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Internal server error at Microsoft

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Model deployment not found

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Invalid API key authentication

Question 13

You are building an agentic application that uses function calling. The model frequently hallucinates arguments for complex tool schemas. Which technique should you apply to improve function calling reliability?

Accepted Answer

Enhance the JSON schema with detailed parameter descriptions, enum constraints, and strict schema validation settings.. Detailed parameter descriptions, enum constraints, and strict schema validation give the model explicit guidance on expected argument formats and allowed values, reducing ambiguity that leads to hallucinated arguments. Strict schema validation (e.g., JSON Schema strict mode) rejects malformed outputs and forces the model to conform. This is the most direct and effective technique for improving function-calling reliability.

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Convert all tool arguments into unstructured natural language text.

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Remove all parameter descriptions from the tool schema to reduce prompt token count.

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Set the model frequency penalty to high values.

Question 14

You are designing an enterprise RAG solution using Azure AI Search and Azure OpenAI. You need to ensure data security and compliance across different user permission tiers. Which TWO actions should you implement? (Choose two.)

Accepted Answer

Ensure all data transfers between Azure AI Search and Azure OpenAI utilize TLS 1.2 or higher.. Option A is correct because Azure AI Search and Azure OpenAI communicate over HTTPS endpoints, and enforcing TLS 1.2 or higher protects data in transit against interception or downgrade attacks, which is a baseline requirement for enterprise security and compliance. Option B is correct because Azure AI Search supports document-level access control via security filters (for example, using the user's group IDs in a filterable field or the built-in ACL/rbacScope fields), ensuring each permission tier only retrieves documents its users are authorized to see, which is essential in a multi-tenant RAG solution. Option C is wrong because storing unencrypted authentication tokens in embedding metadata exposes credentials to anyone querying the index and violates token-handling best practices. Option D is wrong because granting admin privileges to all users removes least-privilege enforcement and does not address retrieval latency in a legitimate way. Option E is wrong because disabling managed identities and embedding plaintext connection strings in source code leaks secrets and discards Azure's keyless, identity-based authentication model.

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Store unencrypted user authentication tokens directly inside the embedding vector metadata.

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Grant every user application admin privileges to bypass retrieval latency checks.

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Disable managed identities and store plaintext connection strings in the application source code.

Question 15

You are implementing a multi-turn conversational agent in Azure AI Foundry. As the conversation grows longer, API latency and costs increase significantly. Which strategy should you implement to manage the conversation history effectively?

Accepted Answer

Implement a sliding window history buffer combined with periodic summarization of older dialogue turns.. For long multi-turn conversations, the standard pattern is a sliding window that keeps only the most recent N turns plus a running summary of older dialogue. This bounds the token count sent to the model, controlling latency and cost, while preserving essential context through summarization. It is the recommended approach in Azure AI Foundry for managing conversation history.

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Increase max_tokens to accommodate infinite history growth.

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Disable the chat history array entirely and treat every turn as a standalone prompt.

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Truncate the entire conversation history after every single user turn.